{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:03.910254Z",
     "start_time": "2020-11-19T00:51:03.202765Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from collections import Counter\n",
    "\n",
    "sns.set_style('ticks')\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 300\n",
    "mpl.rc(\"savefig\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Read files and select drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:03.917152Z",
     "start_time": "2020-11-19T00:51:03.911986Z"
    }
   },
   "outputs": [],
   "source": [
    "# log2_median_ic50, log2_median_ic50_9f, log2_median_ic50_hn, log2_median_ic50_3f_hn, log2_median_ic50_9f_hn, log2_max_conc, log2_median_ic50_3f_hn\n",
    "ref_type = 'log2_median_ic50_hn' # log2_median_ic50_3f_hn | log2_median_ic50_hn\n",
    "model_name = 'hn_drug_cw_dw10_100000_model2' # hn_drug_cw_dw10_100000_model | hn_drug_cw_dw1_100000_model | hn_drug_cw_dwsim10_100000_model\n",
    "\n",
    "# for each patient, if cell cluster is less than 5%, then we don't consider that cluster \n",
    "freq_cutoff = 0.05\n",
    "\n",
    "# shift the dosage as GDSC experiment (Syto60) is less sensitive\n",
    "dosage_shifted = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:04.354726Z",
     "start_time": "2020-11-19T00:51:04.322286Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(81, 27)\n"
     ]
    }
   ],
   "source": [
    "drug_info_df = pd.read_csv('../preprocessed_data/GDSC/hn_drug_stat.csv', index_col=0)\n",
    "drug_info_df.index = drug_info_df.index.astype(str)\n",
    "\n",
    "drug_id_name_dict = dict(zip(drug_info_df.index, drug_info_df['Drug Name']))\n",
    "print (drug_info_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:04.861234Z",
     "start_time": "2020-11-19T00:51:04.852499Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tested_drug_list = [1032, 1007, 133, 201, 1010] + [182, 301, 302]\n",
    "[d for d in tested_drug_list if d not in drug_info_df.index.astype(int)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Read predicted IC50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:06.350494Z",
     "start_time": "2020-11-19T00:51:06.348062Z"
    }
   },
   "outputs": [],
   "source": [
    "norm_type = 'TPM'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:11.589054Z",
     "start_time": "2020-11-19T00:51:11.578044Z"
    }
   },
   "outputs": [],
   "source": [
    "cadrres_cluster_df = pd.read_csv('../result/HN_model/{}/pred_gdsc_no_bias_{}.csv'.format(norm_type, model_name), index_col=0)\n",
    "out_dir = '../result/HN_model/{}/'.format(norm_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:11.981861Z",
     "start_time": "2020-11-19T00:51:11.959788Z"
    }
   },
   "outputs": [
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       "      <td>1.518215</td>\n",
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       "      <td>0.909347</td>\n",
       "      <td>-7.032869</td>\n",
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       "      <td>1.614273</td>\n",
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       "      <td>1.695725</td>\n",
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       "      <th>C1</th>\n",
       "      <td>10.708415</td>\n",
       "      <td>-5.777905</td>\n",
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       "         1001      1003      1004      1006      1007      1010      1012  \\\n",
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       "\n",
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       "B2  3.192027  4.276430 -1.907378  ...  0.916235  2.221327  0.359681  3.609359   \n",
       "C1  2.172377  3.017235 -1.196119  ...  2.178965  3.118207  1.886971  3.914361   \n",
       "\n",
       "         305       306       308       328       331       346  \n",
       "A1  3.715341  2.516973 -0.655629 -1.366619  1.653326 -3.889950  \n",
       "A2  3.321205  2.365288 -1.005312 -2.153061  1.553563 -4.294621  \n",
       "B1  4.209246  2.785194  1.595900  0.226684  1.879187 -1.331815  \n",
       "B2  3.819401  2.403583  1.092272 -0.608327  1.695725 -1.984964  \n",
       "C1  4.348190  3.139670  0.215174 -0.739603  2.312843 -2.266522  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cadrres_cluster_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:12.523384Z",
     "start_time": "2020-11-19T00:51:12.518389Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81 24\n"
     ]
    }
   ],
   "source": [
    "drug_list = drug_info_df.index\n",
    "cluster_list = cadrres_cluster_df.index\n",
    "print(len(drug_list), len(cluster_list))\n",
    "\n",
    "drug_info_df = drug_info_df.loc[drug_list]\n",
    "cadrres_cluster_df = cadrres_cluster_df[drug_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:13.061468Z",
     "start_time": "2020-11-19T00:51:13.059050Z"
    }
   },
   "outputs": [],
   "source": [
    "if dosage_shifted:\n",
    "    # Shift by 4 uM\n",
    "    cadrres_cluster_df = cadrres_cluster_df - 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Read cell cluster % in each patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:13.966352Z",
     "start_time": "2020-11-19T00:51:13.915041Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cluster</th>\n",
       "      <th>A1</th>\n",
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       "      <th>patient_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HN120</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.313187</td>\n",
       "      <td>0.175824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.120879</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.032967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN137</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005682</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340909</td>\n",
       "      <td>0.085227</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096591</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.011364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN148</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.313514</td>\n",
       "      <td>0.205405</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.021622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN159</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.317365</td>\n",
       "      <td>0.185629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.48503</td>\n",
       "      <td>0.011976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN160</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.422222</td>\n",
       "      <td>0.414815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.162963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "cluster           A1        A2        B1        B2        C1        C2  \\\n",
       "patient_id                                                               \n",
       "HN120       0.010989  0.005495  0.000000  0.000000  0.000000  0.000000   \n",
       "HN137       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "HN148       0.000000  0.000000  0.000000  0.000000  0.313514  0.205405   \n",
       "HN159       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "HN160       0.000000  0.000000  0.422222  0.414815  0.000000  0.000000   \n",
       "\n",
       "cluster           D1        D2        E1        E2  ...        F3        G1  \\\n",
       "patient_id                                          ...                       \n",
       "HN120       0.313187  0.175824  0.000000  0.000000  ...  0.000000  0.340659   \n",
       "HN137       0.005682  0.000000  0.340909  0.085227  ...  0.096591  0.000000   \n",
       "HN148       0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "HN159       0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "HN160       0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "\n",
       "cluster           G2        H1        I1        I2   J1   J2       K1  \\\n",
       "patient_id                                                              \n",
       "HN120       0.120879  0.000000  0.000000  0.000000  0.0  0.0  0.00000   \n",
       "HN137       0.000000  0.000000  0.000000  0.000000  0.0  0.0  0.00000   \n",
       "HN148       0.000000  0.459459  0.000000  0.000000  0.0  0.0  0.00000   \n",
       "HN159       0.000000  0.000000  0.317365  0.185629  0.0  0.0  0.48503   \n",
       "HN160       0.000000  0.000000  0.000000  0.000000  0.0  0.0  0.00000   \n",
       "\n",
       "cluster            L  \n",
       "patient_id            \n",
       "HN120       0.032967  \n",
       "HN137       0.011364  \n",
       "HN148       0.021622  \n",
       "HN159       0.011976  \n",
       "HN160       0.162963  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_df = pd.read_excel('../preprocessed_data/HN_patient_specific/percent_patient_tpm_cluster.xlsx', index_col=[0, 1]).reset_index()\n",
    "freq_df = freq_df.pivot(index='patient_id', columns='cluster', values='percent').fillna(0) / 100\n",
    "\n",
    "patient_list = freq_df.index\n",
    "\n",
    "freq_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### List all pairs of patient and drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:16.732471Z",
     "start_time": "2020-11-19T00:51:16.687269Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "pred_delta_df = pd.DataFrame(cadrres_cluster_df.values - drug_info_df[ref_type].values, columns=drug_list, index=cluster_list)\n",
    "pred_cv_df = 100 / (1 + (np.power(2, -pred_delta_df)))\n",
    "pred_kill_df = 100 - pred_cv_df\n",
    "\n",
    "rows = []\n",
    "for p in patient_list:\n",
    "    c_list = freq_df.loc[p][freq_df.loc[p] >= freq_cutoff].index.values\n",
    "    freqs = freq_df.loc[p][freq_df.loc[p] >= freq_cutoff].values\n",
    "\n",
    "    ##### freq sum to 1 (not in use) #####\n",
    "    # freqs = freqs / np.sum(freqs)\n",
    "\n",
    "    p_pred_delta_weighted = np.matmul(pred_delta_df.loc[c_list].values.T, freqs)\n",
    "    p_pred_delta_mat = pred_delta_df.loc[c_list].values\n",
    "    \n",
    "    p_pred_kill_weighted = np.matmul(pred_kill_df.loc[c_list].values.T, freqs)\n",
    "    p_pred_kill_mat = pred_kill_df.loc[c_list].values\n",
    "\n",
    "    for d_i, d_id in enumerate(drug_list):\n",
    "        rows += [[p, d_id] + ['|'.join(c_list)] + ['|'.join([\"{:.14}\".format(f) for f in freqs])] + \n",
    "                 ['|'.join([\"{:.14}\".format(f) for f in p_pred_delta_mat[:, d_i]])] + \n",
    "                 [\"{:.14}\".format(p_pred_delta_weighted[d_i])] +\n",
    "                 ['|'.join([\"{:.14}\".format(f) for f in p_pred_kill_mat[:, d_i]])] + \n",
    "                 [\"{:.14}\".format(p_pred_kill_weighted[d_i])]\n",
    "                ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:17.488490Z",
     "start_time": "2020-11-19T00:51:17.472728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster_p</th>\n",
       "      <th>cluster_delta</th>\n",
       "      <th>delta</th>\n",
       "      <th>cluster_kill</th>\n",
       "      <th>kill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1001</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>0.97886227658824|0.85126533199626|0.8324244987...</td>\n",
       "      <td>0.83415414397391</td>\n",
       "      <td>33.659714568654|35.662288537193|35.96248678207...</td>\n",
       "      <td>33.510912108776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1003</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>1.201525430967|1.2213483950695|1.7510220758333...</td>\n",
       "      <td>1.3523171038281</td>\n",
       "      <td>30.304617687459|30.015199140281|22.90442054356...</td>\n",
       "      <td>25.954725903113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1004</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>1.3994447964209|1.122611275271|1.7370480948823...</td>\n",
       "      <td>1.4123186054551</td>\n",
       "      <td>27.487627363317|31.472200982455|23.07590797174...</td>\n",
       "      <td>25.113006747586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1006</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>0.95692434487753|1.0291964548272|2.06647006019...</td>\n",
       "      <td>1.3661156656751</td>\n",
       "      <td>34.000107841096|32.885139449453|19.27300067146...</td>\n",
       "      <td>26.150905313026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>3.1869156780876|2.7761284650153|4.173928870489...</td>\n",
       "      <td>3.373717068682</td>\n",
       "      <td>9.8945048311346|12.738664907768|5.249337148951...</td>\n",
       "      <td>7.9097567173705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  patient drug_id      cluster  \\\n",
       "0   HN120    1001  D1|D2|G1|G2   \n",
       "1   HN120    1003  D1|D2|G1|G2   \n",
       "2   HN120    1004  D1|D2|G1|G2   \n",
       "3   HN120    1006  D1|D2|G1|G2   \n",
       "4   HN120    1007  D1|D2|G1|G2   \n",
       "\n",
       "                                           cluster_p  \\\n",
       "0  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "1  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "2  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "3  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "4  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "\n",
       "                                       cluster_delta             delta  \\\n",
       "0  0.97886227658824|0.85126533199626|0.8324244987...  0.83415414397391   \n",
       "1  1.201525430967|1.2213483950695|1.7510220758333...   1.3523171038281   \n",
       "2  1.3994447964209|1.122611275271|1.7370480948823...   1.4123186054551   \n",
       "3  0.95692434487753|1.0291964548272|2.06647006019...   1.3661156656751   \n",
       "4  3.1869156780876|2.7761284650153|4.173928870489...    3.373717068682   \n",
       "\n",
       "                                        cluster_kill             kill  \n",
       "0  33.659714568654|35.662288537193|35.96248678207...  33.510912108776  \n",
       "1  30.304617687459|30.015199140281|22.90442054356...  25.954725903113  \n",
       "2  27.487627363317|31.472200982455|23.07590797174...  25.113006747586  \n",
       "3  34.000107841096|32.885139449453|19.27300067146...  26.150905313026  \n",
       "4  9.8945048311346|12.738664907768|5.249337148951...  7.9097567173705  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_drug_pred_df = pd.DataFrame(rows, columns=['patient', 'drug_id', 'cluster', 'cluster_p', 'cluster_delta', 'delta', 'cluster_kill', 'kill'])\n",
    "single_drug_pred_df = single_drug_pred_df[['patient', 'drug_id', 'cluster', 'cluster_p', 'cluster_delta', 'delta', 'cluster_kill', 'kill']]\n",
    "single_drug_pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:19.349319Z",
     "start_time": "2020-11-19T00:51:19.342581Z"
    }
   },
   "outputs": [],
   "source": [
    "single_drug_pred_df.loc[:, 'drug_name'] = [drug_id_name_dict[d] for d in single_drug_pred_df['drug_id'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:21.555220Z",
     "start_time": "2020-11-19T00:51:20.981387Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4d63c836d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "single_drug_id_list = ['1007', '133', '201', '1010', '182', '301', '302', '1012']\n",
    "single_drug_list = ['Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib', 'Obatoclax Mesylate', 'PHA-793887', 'PI-103', 'Vorinostat']\n",
    "\n",
    "temp_df = (freq_df.loc[freq_df.index != 'HN182'] >= freq_cutoff).sum()\n",
    "selected_clusters = temp_df.index[temp_df > 0]\n",
    "\n",
    "cmap = plt.cm.get_cmap('bone_r', 10)\n",
    "selected_kill_df = pred_kill_df.loc[selected_clusters, single_drug_id_list]\n",
    "selected_kill_df.columns = single_drug_list\n",
    "selected_kill_df = selected_kill_df[selected_kill_df.sum().sort_values(ascending=False).index]\n",
    "\n",
    "sns.heatmap(selected_kill_df.T, cmap=cmap, vmin=0, vmax=100, linewidth=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:36.560919Z",
     "start_time": "2020-11-19T00:51:36.545616Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug ID</th>\n",
       "      <th>log2_median_ic50_hn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PI-103</th>\n",
       "      <td>302</td>\n",
       "      <td>3.056464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHA-793887</th>\n",
       "      <td>301</td>\n",
       "      <td>3.606539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doxorubicin</th>\n",
       "      <td>133</td>\n",
       "      <td>-2.812230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Obatoclax Mesylate</th>\n",
       "      <td>182</td>\n",
       "      <td>-2.958665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vorinostat</th>\n",
       "      <td>1012</td>\n",
       "      <td>0.711127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B</th>\n",
       "      <td>201</td>\n",
       "      <td>-8.520806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gefitinib</th>\n",
       "      <td>1010</td>\n",
       "      <td>-1.374969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docetaxel</th>\n",
       "      <td>1007</td>\n",
       "      <td>-9.792998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Drug ID  log2_median_ic50_hn\n",
       "PI-103                 302             3.056464\n",
       "PHA-793887             301             3.606539\n",
       "Doxorubicin            133            -2.812230\n",
       "Obatoclax Mesylate     182            -2.958665\n",
       "Vorinostat            1012             0.711127\n",
       "Epothilone B           201            -8.520806\n",
       "Gefitinib             1010            -1.374969\n",
       "Docetaxel             1007            -9.792998"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log2_median_ic50_df = drug_info_df.loc[single_drug_id_list][['Drug Name', ref_type]].reset_index().set_index('Drug Name')\n",
    "log2_median_ic50_df.loc[selected_kill_df.sum().sort_values(ascending=False).index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:41.729222Z",
     "start_time": "2020-11-19T00:51:41.723036Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log2_median_ic50_hn\n",
      "8.319312\n",
      "12.180817\n",
      "0.142375\n",
      "0.128633\n",
      "1.637083\n",
      "0.002723\n",
      "0.385561\n",
      "0.001127\n"
     ]
    }
   ],
   "source": [
    "print (ref_type)\n",
    "log2_median_ic50s = log2_median_ic50_df.loc[selected_kill_df.sum().sort_values(ascending=False).index][ref_type].values\n",
    "median_ic50s = np.power(2, log2_median_ic50s)\n",
    "\n",
    "for x in median_ic50s:\n",
    "    print ('{:f}'.format(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:42.476409Z",
     "start_time": "2020-11-19T00:51:42.469543Z"
    }
   },
   "outputs": [],
   "source": [
    "temp_df = freq_df.loc[freq_df.index != 'HN182'].stack().reset_index()\n",
    "temp_df = temp_df[temp_df[0] >= freq_cutoff]\n",
    "patient_cluster_dict = dict(zip(temp_df['cluster'], temp_df['patient_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:42.989387Z",
     "start_time": "2020-11-19T00:51:42.984796Z"
    }
   },
   "outputs": [],
   "source": [
    "patient_list = sorted(list(set(temp_df['patient_id'])))\n",
    "cluster_list = temp_df['cluster'].values\n",
    "\n",
    "cmap = plt.cm.get_cmap('tab10', 10)\n",
    "patient_color_dict = dict(zip(patient_list, [cmap(c) for c in range(len(patient_list))]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:43.318191Z",
     "start_time": "2020-11-19T00:51:43.314559Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HN120', 'HN137', 'HN148', 'HN159', 'HN160']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:43.759852Z",
     "start_time": "2020-11-19T00:51:43.757061Z"
    }
   },
   "outputs": [],
   "source": [
    "col_colors = [patient_color_dict[patient_cluster_dict[c]] for c in cluster_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:47.513213Z",
     "start_time": "2020-11-19T00:51:46.561663Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2400x1650 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.get_cmap('bone_r', 11)\n",
    "cg = sns.clustermap(selected_kill_df.T[cluster_list], cmap=cmap, vmin=-10, vmax=100, linewidth=0.5, row_cluster=False, col_cluster=False, col_colors=col_colors, figsize=(8,5.5))\n",
    "\n",
    "_ = cg.ax_heatmap.set_xlabel('Cluster ID')\n",
    "plt.tight_layout()\n",
    "# _ = cg.cax.set_visible(False)\n",
    "\n",
    "# cg.savefig('../figure/Fig4A_heatmap_cluster_pred.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T00:51:53.852237Z",
     "start_time": "2020-11-19T00:51:53.831471Z"
    }
   },
   "outputs": [],
   "source": [
    "if dosage_shifted:\n",
    "    single_drug_pred_df.to_csv(out_dir + 'pred_drug_kill_{}_{}_shifted.csv'.format(ref_type, model_name), index=False)\n",
    "else:\n",
    "    single_drug_pred_df.to_csv(out_dir + 'pred_drug_kill_{}_{}.csv'.format(ref_type, model_name), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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